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Boost LinkedIn Automation: Creating Content with n8n and Google Gemini

Updated
•9 min read
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Pushpendra B

In the fast-paced world of social media, consistency is key. But for busy professionals and tech enthusiasts, maintaining a steady stream of high-quality LinkedIn posts can be a challenge.

In this post, I'll walk you through how I automated my LinkedIn content creation using n8n, Google Gemini, and a human-in-the-loop workflow. This system allows me to generate engaging, platform-specific content from a simple topic, review it, add visual assets, and publish it—all without leaving the automation flow.

The Workflow Overview

The core of this automation is an n8n workflow that acts as my social media assistant. Here’s the high-level logic:

  1. Idea Injection: I provide a simple "Topic" or "Title" via a web form.

  2. AI Processing: An AI Agent (powered by Google Gemini) researches the topic and drafts a professional LinkedIn post, complete with hashtags and a call-to-action.

  3. Human Review: The workflow pauses and presents the generated text to me. I can review it and uploading a relevant image.

  4. Publishing: Once approved, the workflow automatically posts the text and image to my LinkedIn profile.

The Automation Architecture

Here is the visual representation of the flow:

The n8n Canvas View

Here's what the actual workflow looks like in n8n:

The workflow is organized into 4 distinct sections (marked by sticky notes):

  1. Get the Data for Social Media Post using the Web Form (Yellow)

  2. AI Agent will do its Job (Blue)

  3. Get Image to Be Published (Light Blue)

  4. Publish on Social Media (Yellow) → Confirmation (Green)

Step-by-Step Node-by-Node Implementation Guide

Now, let me walk you through building this workflow from scratch. Each subsection represents a node you need to add to your n8n canvas.

Section 1: Get the Data for Social Media Post

Node 1: On form submission (Form Trigger)

  • Node Type: Form Trigger

  • Purpose: This is the entry point of your workflow. It creates a web form to capture the post topic.

  • Configuration:

    • Form Title: "Social Media Content AI Agent"

    • Form Description: Add a helpful description explaining what the form does

    • Form Fields:

      • Field Name: Post Title/Topic

      • Field Type: Text

      • Placeholder: "Write a brief and clear title or main topic for the post"

    • Authentication: Basic Auth (optional, for security)

    • Button Label: "Continue to Image Upload"

  • What happens: When submitted, this triggers the workflow and passes the topic to the next node.

Node 2: Split Form Input (Set Node)

  • Node Type: Set (Edit Fields)

  • Purpose: Extract and structure the data from the form submission.

  • Configuration:

    • Mode: Keep only specific fields

    • Fields to Include: output.platform_posts.LinkedIn.post

  • Connect to: Output from On form submission

Node 3: Split Data (Set Node)

  • Node Type: Set (Edit Fields)

  • Purpose: Further refine the data structure.

  • Configuration: Same as Split Form Input

  • Connect to: Output from Split Form Input

Node 4: Data for AI (Set Node)

  • Node Type: Set (Edit Fields)

  • Purpose: Prepare the final input data for the AI Agent.

  • Configuration:

    • Assignments:

      • Post Title/Topic = {{ $('On form submission').item.json['Post Title/Topic'] }}

      • formMode = {{ $('On form submission').item.json.formMode }}

  • Connect to: Output from Split Data

Section 2: AI Agent Will Do Its Job

Node 5: AI Agent (AI Agent Node)

  • Node Type: @n8n/n8n-nodes-langchain.agent

  • Purpose: The brain of the operation. Generates platform-specific content.

  • Configuration:

    • Prompt Type: Define

    • Prompt: A detailed system prompt that:

      • Defines the AI's role as a content creator for your brand

      • Specifies platform-specific rules (LinkedIn, Instagram, Facebook, Twitter)

      • Includes hashtag strategies

      • References the input data: {{ $json['Post Title/Topic'] }}

    • Has Output Parser: ✓ Enabled

  • Connect to: Output from Data for AI

  • Sub-nodes to connect:

    • Google Gemini Chat Model (Language Model)

    • Structured Output Parser (Output Parser)

Node 6: Google Gemini Chat Model (Sub-node)

  • Node Type: @n8n/n8n-nodes-langchain.lmChatGoogleGemini

  • Purpose: The actual AI model that processes the prompt.

  • Configuration:

    • Model Name: models/gemini-3-flash-preview (or your preferred Gemini model)

    • Credentials: Google Gemini API credentials

  • Connect to: AI Agent (via Language Model connection)

Node 7: Structured Output Parser (Sub-node)

  • Node Type: @n8n/n8n-nodes-langchain.outputParserStructured

  • Purpose: Ensures the AI returns properly formatted JSON.

  • Configuration:

    • Schema Type: Manual

    • Input Schema: A JSON schema defining the structure:

{
  "type": "object",
  "properties": {
    "platform_posts": {
      "type": "object",
      "properties": {
        "LinkedIn": {
          "type": "object",
          "properties": {
            "post": {"type": "string"},
            "hashtags": {"type": "array"},
            "call_to_action": {"type": "string"}
          }
        },
        "Twitter": {...},
        "Facebook": {...}
      }
    }
  }
}
  • Connect to: AI Agent (via Output Parser connection)

Node 8: Aggregate (Aggregate Node)

  • Node Type: Aggregate

  • Purpose: Combines all the AI output into a single item for easier reference.

  • Configuration:

    • Aggregate: All Item Data

  • Connect to: Output from AI Agent

Section 3: Get Image to Be Published

Node 9: Upload Image (Form Node)

  • Node Type: Form

  • Purpose: Pauses the workflow to let you review the AI-generated text and upload an image.

  • Configuration:

    • Operation: Wait for Form Submission

    • Form Title: "Review the Text"

    • Form Description: Display the AI-generated text using expressions:

        LinkedIn: {{ $json.data[0].output.platform_posts.LinkedIn.post }}
  Twitter: {{ $json.data[0].output.platform_posts.Twitter.post }}

    • Form Fields:

      • Field Label: image

      • Field Type: File

      • Accepted File Types: .jpg (or .png, .jpeg)

      • Required: ✓ Yes

    • Button Label: "Proceed to Next Step"

  • Connect to: Output from Aggregate

Node 10: Nest Top Meta (Set Node)

  • Node Type: Set (Edit Fields)

  • Purpose: Preserve all form data including the binary image.

  • Configuration:

    • Assignments:

      • Name: metaTop

      • Type: Object

      • Value: {{ $json }}

    • Options: Include Binary ✓

  • Connect to: Output from Upload Image

Node 11: Rename Image Binary Top Image (Code Node)

  • Node Type: Code

  • Purpose: Rename the binary data field for LinkedIn compatibility.

  • Configuration:

    • Mode: Run Once for Each Item

    • JavaScript Code:

$input.item.binary.top = $input.item.binary.data;
delete $input.item.binary.data;
return $input.item;
  • Connect to: Output from Nest Top Meta

Section 4: Publish on Social Media

Node 12: Publish to LinkedIn (LinkedIn Node)

  • Node Type: LinkedIn

  • Purpose: Posts the content to your LinkedIn profile.

  • Configuration:

    • Resource: Post

    • Operation: Create

    • Person: Your LinkedIn Person URN (e.g., CryRqQfSsC)

    • Text:

        {{ $('AI Agent').item.json.output.platform_posts.LinkedIn.post }}
  {{ $('Aggregate').item.json.data[0].output.platform_posts.LinkedIn.call_to_action }}

    • Share Media Category: IMAGE

    • Binary Property Name: image

    • Credentials: LinkedIn OAuth2 credentials

  • Connect to: Output from Rename Image Binary Top Image

Node 13: X (Twitter Node) - Optional

  • Node Type: Twitter

  • Purpose: Posts to Twitter/X.

  • Configuration:

    • Text: {{ $('Aggregate').item.json.data[0].output.platform_posts.Twitter.post }}

    • Credentials: Twitter OAuth2 credentials

  • Connect to: Can run in parallel with LinkedIn

Node 14: Edit Fields (Set Node)

  • Node Type: Set (Edit Fields)

  • Purpose: Extract Twitter post ID for confirmation.

  • Configuration:

    • Assignments:

      • Name: edit_history_tweet_ids

      • Type: Array

      • Value: {{ $json.edit_history_tweet_ids }}

  • Connect to: Output from X (if using Twitter)

Node 15: Merge1 (Merge Node)

  • Node Type: Merge

  • Purpose: Combines outputs from LinkedIn (and optionally Twitter) before final confirmation.

  • Configuration: Default merge settings

  • Connect to:

    • Input 1: Output from Publish to LinkedIn

    • Input 2: Any other social media nodes

Section 5: Confirmation that Post is Published

Node 16: Form (Form Node - Completion)

  • Node Type: Form

  • Purpose: Shows a success message with links to the published posts.

  • Configuration:

    • Operation: Completion

    • Completion Title: "Thanks"

    • Completion Message:

        Your post has successfully been submitted to LinkedIn.

  LinkedIn: https://www.linkedin.com/feed/update/{{ $('Publish to LinkedIn').item.json.urn }}

  Thanks,
  AI Agent

    • Form Title: "AI Agent (Job Done)"

  • Connect to: Output from Merge1

Deep Dive: How It Works

1. The Trigger (Command Center)

Everything starts with an n8n Form Trigger. Instead of manually logging into LinkedIn, I simply open a private URL hosted by my n8n instance. This form asks for:

  • Post Title/Topic: e.g., "The Future of Open Source AI".

  • Keywords: Optional context to guide the AI.

2. The Brain: Google Gemini AI Agent

This is where the magic happens. The input is passed to an AI Agent node connected to the Google Gemini Chat Model.

I've configured the prompt to act as a "Content Creation AI" for my brand. It follows specific rules:

  • Tone: Professional, insightful, and value-driven.

  • Structure: 3-4 sentences, optimized for engagement.

  • Hashtags: A mix of general tech tags (#Innovation, #AI) and niche ones.

The agent doesn't just output text; it uses a Structured Output Parser to return a clean JSON object containing the post text, hashtags, and even suggestions for other platforms like Twitter and Facebook.

3. Human-in-the-Loop (The "Wait" Node)

Automation is great, but I don't want to blindly post AI-generated content. I need final approval.

The flow uses a second n8n Form node (titled "Upload Image") in the middle of the execution.

  • The workflow pauses here.

  • It displays the AI-generated text for me to read.

  • It asks me to upload the final image or creative asset to go with the post.

  • Once I hit "Proceed," the workflow resumes.

This step is crucial. It combines the speed of AI with the quality control of a human.

4. The Publisher

Finally, the workflow takes the text approved in the previous step and the image I just uploaded. It formats the binary data and sends it to the LinkedIn node, which uses the LinkedIn API to create a "Share" on my personal profile.

Security: Keeping Credentials Safe

One of the most important aspects of sharing or backing up automation flows is security.

  • Credential Separation: n8n separates the workflow logic (the nodes and connections) from the credentials (API keys and passwords).

  • No Hardcoded Secrets: In the workflow JSON file, you will never see my actual API keys. You will only see references like linkedInOAuth2Api or googlePalmApi.

  • Environment Variables: For sensitive data that might be needed inside expressions, I use n8n environment variables rather than typing them directly into the node parameters.

When you import my flow, n8n will ask you to set up your own credentials for LinkedIn and Google Gemini. Your secrets stay on your server, and mine stay on mine.

Conclusion

This workflow has transformed how I manage my professional presence. By automating the "drafting" phase and streamlining the "publishing" phase, I save hours of time while maintaining high content quality.

If you want to try this yourself, you'll need:

  1. A self-hosted or cloud n8n instance.

  2. A Google Cloud Console project with the Gemini API enabled.

  3. A LinkedIn App for API access.

Happy Automating!

#programming#n8n#n8n-automation#ai-automation

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